code: def backward(ctx: Any, *grad_outputs: Any) -> Any:
      aggregator: "SSHENeuralNetworkAggregator" = ctx.aggregator
      ha = (
          ctx.saved_tensors[0]
          if aggregator.ctx.rank == aggregator.rank_a
          else None
      )
      hb = (
          ctx.saved_tensors[0]
          if aggregator.ctx.rank == aggregator.rank_b
          else None
      )
      dz = (
          grad_outputs[0]
          if aggregator.ctx.rank == aggregator.rank_b
          else None
      )
      return aggregator.backward(dz, ha, hb), None
  
imports: import typing
  from typing import Any, Iterator
  import torch
  from torch.nn import Parameter
  from fate.arch.context import Context
  from fate.arch.protocol.mpc.common.encoding import IgnoreEncodings
  from fate.arch.protocol.mpc.mpc import FixedPointEncoder
  from fate.arch.trace import auto_trace
label_desc: Use image node_id 2 for calling the SSHENeuralNetworkAggregatorFunction obj's underlying member method code with example usage: obj.backward(ctx) and returns: aggregator,  None